Scholar
Zhuotao Tian
Google Scholar ID: mEjhz-IAAAAJ
Professor, Harbin Institute of Technology (Shenzhen)
Citations & Impact
Citations
3,970
H-index
27
i10-index
32
Publications
20
Co-authors
17
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Published over 40 top-tier conference and journal papers in CCF-A/ Tsinghua A-class venues in the past five years, including several high-quality or selected articles as first or corresponding author: TPAMI (IEEE flagship journal in AI), CVPR Highlight/Oral, AAAI Oral, and ACM MM Oral. The proposed multimodal segmentation perception large model LISA (CVPR 2024, Oral) was selected as one of the most influential works at CVPR 2024 (0.06%, 7/11532). Has 13 authorized national invention patents (9 as the first inventor) and led a team to win the second place in the ICDAR 2019 International Scene Text Recognition Challenge.
Research Experience
- Worked as a researcher at SmartMore Group in Hong Kong, focusing on multimodal perception and smart manufacturing. Several visual perception and model lightweighting technologies have been applied in various industrial sectors.
Education
- Earned his bachelor's degree from the Elite Class in Computer Science and Technology ("Mount Everest Program," now under the School of Future Technology) at the Harbin Institute of Technology (main campus). During his studies, he received the National Scholarship (top 1%), the China Aerospace Science and Technology Corporation (CASC) Scholarship (top 2%), and multiple First-Class People's Scholarships (top 3%). Obtained his Ph.D. from the Department of Computer Science and Engineering at The Chinese University of Hong Kong, under the supervision of IEEE Fellow Professor Jiaya Jia and Professor Bei Yu.
Background
- Primary research focuses on deep learning, visual perception, large-scale models, and multimodal perception. Selected as a 2024 National-Level High-Level Young Talent and conducts research with funding from the CCF-Tencent Rhino-Bird Fund and the Huawei Strategic Research Institute Talent Fund.
Miscellany
- Welcomes outstanding undergraduate students for early collaboration and communication. Students who perform well can be given priority for recommendation to the group (limited slots, decided based on time in the group and output). Outstanding bachelor's or master's graduates can be recommended to top computer vision research teams at the University of Hong Kong, the Chinese University of Hong Kong, and the Hong Kong University of Science and Technology for doctoral studies, and also to renowned companies for internships and employment. Regular weekly formal meetings are held to ensure effective learning. Mutual respect and two-way selection.
